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1.
Cancers (Basel) ; 13(20)2021 Oct 18.
Artigo em Inglês | MEDLINE | ID: mdl-34680372

RESUMO

Pancreatic ductal adenocarcinoma (PDAC) is a devastating condition characterised by vague symptomatology and delayed diagnosis. About 30% of PDAC patients report a history of new onset diabetes, usually diagnosed within 3 years prior to the diagnosis of cancer. Thus, new onset diabetes, which is also known as pancreatic cancer-related diabetes (PCRD), could be a harbinger of PDAC. Diabetes is driven by progressive ß cell loss/dysfunction and insulin resistance, two key features that are also found in PCRD. Experimental studies suggest that PDAC cell-derived exosomes carry factors that are detrimental to ß cell function and insulin sensitivity. However, the role of stromal cells, particularly pancreatic stellate cells (PSCs), in the pathogenesis of PCRD is not known. PSCs are present around the earliest neoplastic lesions and around islets. Given that PSCs interact closely with cancer cells to drive cancer progression, it is possible that exosomal cargo from both cancer cells and PSCs plays a role in modulating ß cell function and peripheral insulin resistance. Identification of such mediators may help elucidate the mechanisms of PCRD and aid early detection of PDAC. This paper discusses the concept of a novel role of PSCs in the pathogenesis of PCRD.

2.
Pancreas ; 50(7): 916-922, 2021 Aug 01.
Artigo em Inglês | MEDLINE | ID: mdl-34629446

RESUMO

ABSTRACT: The potential of artificial intelligence (AI) applied to clinical data from electronic health records (EHRs) to improve early detection for pancreatic and other cancers remains underexplored. The Kenner Family Research Fund, in collaboration with the Cancer Biomarker Research Group at the National Cancer Institute, organized the workshop entitled: "Early Detection of Pancreatic Cancer: Opportunities and Challenges in Utilizing Electronic Health Records (EHR)" in March 2021. The workshop included a select group of panelists with expertise in pancreatic cancer, EHR data mining, and AI-based modeling. This review article reflects the findings from the workshop and assesses the feasibility of AI-based data extraction and modeling applied to EHRs. It highlights the increasing role of data sharing networks and common data models in improving the secondary use of EHR data. Current efforts using EHR data for AI-based modeling to enhance early detection of pancreatic cancer show promise. Specific challenges (biology, limited data, standards, compatibility, legal, quality, AI chasm, incentives) are identified, with mitigation strategies summarized and next steps identified.

3.
Pancreatology ; 21(8): 1524-1530, 2021 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-34507900

RESUMO

BACKGROUND & AIMS: Increased intrapancreatic fat is associated with pancreatic diseases; however, there are no established objective diagnostic criteria for fatty pancreas. On non-contrast computed tomography (CT), adipose tissue shows negative Hounsfield Unit (HU) attenuations (-150 to -30 HU). Using whole organ segmentation on non-contrast CT, we aimed to describe whole gland pancreatic attenuation and establish 5th and 10th percentile thresholds across a spectrum of age and sex. Subsequently, we aimed to evaluate the association between low pancreatic HU and risk of pancreatic ductal adenocarcinoma (PDAC). METHODS: The whole pancreas was segmented in 19,456 images from 469 non-contrast CT scans. A convolutional neural network was trained to assist pancreas segmentation. Mean pancreatic HU, volume, and body composition metrics were calculated. The lower 5th and 10th percentile for mean pancreatic HU were identified, examining the association with age and sex. Pre-diagnostic CT scans from patients who later developed PDAC were compared to cancer-free controls. RESULTS: Less than 5th percentile mean pancreatic HU was significantly associated with increase in BMI (OR 1.07; 1.03-1.11), visceral fat (OR 1.37; 1.15-1.64), total abdominal fat (OR 1.12; 1.03-1.22), and diabetes mellitus type 1 (OR 6.76; 1.68-27.28). Compared to controls, pre-diagnostic scans in PDAC cases had lower mean whole gland pancreatic HU (-0.2 vs 7.8, p = 0.026). CONCLUSION: In this study, we report age and sex-specific distribution of pancreatic whole-gland CT attenuation. Compared to controls, mean whole gland pancreatic HU is significantly lower in the pre-diagnostic phase of PDAC.

4.
Mayo Clin Proc Innov Qual Outcomes ; 5(3): 535-541, 2021 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-34195545

RESUMO

Biliary strictures caused by inflammation or fibrosis lead to jaundice and cholangitis which often make it difficult to distinguish malignant strictures. In cases when malignancy cannot be excluded, surgery is often performed. The concept of immunoglobulin G4 (IgG4)-related sclerosing cholangitis (SC) as a benign biliary stricture was recently proposed. The high prevalence of the disease in Asian countries has resulted in multiple diagnostic and treatment guidelines; however, there is need to formulate a standardized diagnostic strategy among various countries considering the utility, invasiveness, and cost-effectiveness. We evaluated accuracies of various diagnostic modalities for biliary strictures comparing pathology in the Delphi meetings which were held in Rochester, MN. The diagnostic utility for each modality was graded according to the experts, including gastroenterologists, endoscopists, radiologists, and pathologists from the United States and Japan. Diagnostic utility of 10 modalities, including serum IgG4 level, noninvasive imaging, endoscopic ultrasound, endoscopic retrograde cholangiopancreatography-related diagnostic procedures were advocated and the reasons were specified. Serum IgG4 level, noninvasive imaging, diagnostic endoscopic ultrasound and intraductal ultrasonography under endoscopic retrograde cholangiopancreatography were recognized as useful modalities for the diagnosis. The information in this article will aid in the diagnosis of biliary strictures particularly for distinguishing IgG4-SC from cholangiocarcinoma and/or primary SC.

6.
Pancreatology ; 21(5): 1001-1008, 2021 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-33840636

RESUMO

OBJECTIVE: Quality gaps in medical imaging datasets lead to profound errors in experiments. Our objective was to characterize such quality gaps in public pancreas imaging datasets (PPIDs), to evaluate their impact on previously published studies, and to provide post-hoc labels and segmentations as a value-add for these PPIDs. METHODS: We scored the available PPIDs on the medical imaging data readiness (MIDaR) scale, and evaluated for associated metadata, image quality, acquisition phase, etiology of pancreas lesion, sources of confounders, and biases. Studies utilizing these PPIDs were evaluated for awareness of and any impact of quality gaps on their results. Volumetric pancreatic adenocarcinoma (PDA) segmentations were performed for non-annotated CTs by a junior radiologist (R1) and reviewed by a senior radiologist (R3). RESULTS: We found three PPIDs with 560 CTs and six MRIs. NIH dataset of normal pancreas CTs (PCT) (n = 80 CTs) had optimal image quality and met MIDaR A criteria but parts of pancreas have been excluded in the provided segmentations. TCIA-PDA (n = 60 CTs; 6 MRIs) and MSD(n = 420 CTs) datasets categorized to MIDaR B due to incomplete annotations, limited metadata, and insufficient documentation. Substantial proportion of CTs from TCIA-PDA and MSD datasets were found unsuitable for AI due to biliary stents [TCIA-PDA:10 (17%); MSD:112 (27%)] or other factors (non-portal venous phase, suboptimal image quality, non-PDA etiology, or post-treatment status) [TCIA-PDA:5 (8.5%); MSD:156 (37.1%)]. These quality gaps were not accounted for in any of the 25 studies that have used these PPIDs (NIH-PCT:20; MSD:1; both: 4). PDA segmentations were done by R1 in 91 eligible CTs (TCIA-PDA:42; MSD:49). Of these, corrections were made by R3 in 16 CTs (18%) (TCIA-PDA:4; MSD:12) [mean (standard deviation) Dice: 0.72(0.21) and 0.63(0.23) respectively]. CONCLUSION: Substantial quality gaps, sources of bias, and high proportion of CTs unsuitable for AI characterize the available limited PPIDs. Published studies on these PPIDs do not account for these quality gaps. We complement these PPIDs through post-hoc labels and segmentations for public release on the TCIA portal. Collaborative efforts leading to large, well-curated PPIDs supported by adequate documentation are critically needed to translate the promise of AI to clinical practice.

7.
Pancreas ; 50(3): 251-279, 2021 03 01.
Artigo em Inglês | MEDLINE | ID: mdl-33835956

RESUMO

ABSTRACT: Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly nonspecific. The premise of improved survival through early detection is that more individuals will benefit from potentially curative treatment. Artificial intelligence (AI) methodology has emerged as a successful tool for risk stratification and identification in general health care. In response to the maturity of AI, Kenner Family Research Fund conducted the 2020 AI and Early Detection of Pancreatic Cancer Virtual Summit (www.pdac-virtualsummit.org) in conjunction with the American Pancreatic Association, with a focus on the potential of AI to advance early detection efforts in this disease. This comprehensive presummit article was prepared based on information provided by each of the interdisciplinary participants on one of the 5 following topics: Progress, Problems, and Prospects for Early Detection; AI and Machine Learning; AI and Pancreatic Cancer-Current Efforts; Collaborative Opportunities; and Moving Forward-Reflections from Government, Industry, and Advocacy. The outcome from the robust Summit conversations, to be presented in a future white paper, indicate that significant progress must be the result of strategic collaboration among investigators and institutions from multidisciplinary backgrounds, supported by committed funders.

8.
Pancreatology ; 21(5): 928-937, 2021 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-33775564

RESUMO

BACKGROUND: Chronic pancreatitis is a known risk factor of pancreatic cancer (PDAC). A similar association has been suggested but not demonstrated for autoimmune pancreatitis (AIP). OBJECTIVE: The aim of our study was to identify and analyse all published cases of AIP and PDAC co-occurrence, focusing on the interval between the diagnoses and the cancer site within the pancreas. METHODS: Relevant studies were identified through automatic searches of the MEDLINE, EMBASE, Scopus, and Web of Science databases, and supplemented by manual checks of reference lists in all retrieved articles. Missing/unpublished data were obtained from the authors of relevant publications in the form of pre-prepared questionnaires. RESULTS: A total of 45 cases of PDAC in AIP patients were identified, of which 12 were excluded from the analysis due to suspicions of duplicity or lack of sufficient data. Thirty-one patients (94%) had type 1 AIP. Synchronous occurrence of PDAC and AIP was reported in 11 patients (33%), metachronous in 22 patients (67%). In the metachronous group, the median period between diagnoses was 66.5 months (2-186) and a majority of cancers (86%) occurred more than two years after AIP diagnosis. In most patients (70%), the cancer originated in the part of the pancreas affected by AIP. CONCLUSIONS: In the literature, there are reports on numerous cases of PDAC in AIP patients. PDAC is more frequent in AIP type 1 patients, typically metachronous in character, and generally found in the part of the pancreas affected by AIP.

9.
Med Phys ; 48(5): 2468-2481, 2021 May.
Artigo em Inglês | MEDLINE | ID: mdl-33595105

RESUMO

PURPOSE: To develop a two-stage three-dimensional (3D) convolutional neural networks (CNNs) for fully automated volumetric segmentation of pancreas on computed tomography (CT) and to further evaluate its performance in the context of intra-reader and inter-reader reliability at full dose and reduced radiation dose CTs on a public dataset. METHODS: A dataset of 1994 abdomen CT scans (portal venous phase, slice thickness ≤ 3.75-mm, multiple CT vendors) was curated by two radiologists (R1 and R2) to exclude cases with pancreatic pathology, suboptimal image quality, and image artifacts (n = 77). Remaining 1917 CTs were equally allocated between R1 and R2 for volumetric pancreas segmentation [ground truth (GT)]. This internal dataset was randomly divided into training (n = 1380), validation (n = 248), and test (n = 289) sets for the development of a two-stage 3D CNN model based on a modified U-net architecture for automated volumetric pancreas segmentation. Model's performance for pancreas segmentation and the differences in model-predicted pancreatic volumes vs GT volumes were compared on the test set. Subsequently, an external dataset from The Cancer Imaging Archive (TCIA) that had CT scans acquired at standard radiation dose and same scans reconstructed at a simulated 25% radiation dose was curated (n = 41). Volumetric pancreas segmentation was done on this TCIA dataset by R1 and R2 independently on the full dose and then at the reduced radiation dose CT images. Intra-reader and inter-reader reliability, model's segmentation performance, and reliability between model-predicted pancreatic volumes at full vs reduced dose were measured. Finally, model's performance was tested on the benchmarking National Institute of Health (NIH)-Pancreas CT (PCT) dataset. RESULTS: Three-dimensional CNN had mean (SD) Dice similarity coefficient (DSC): 0.91 (0.03) and average Hausdorff distance of 0.15 (0.09) mm on the test set. Model's performance was equivalent between males and females (P = 0.08) and across different CT slice thicknesses (P > 0.05) based on noninferiority statistical testing. There was no difference in model-predicted and GT pancreatic volumes [mean predicted volume 99 cc (31cc); GT volume 101 cc (33 cc), P = 0.33]. Mean pancreatic volume difference was -2.7 cc (percent difference: -2.4% of GT volume) with excellent correlation between model-predicted and GT volumes [concordance correlation coefficient (CCC)=0.97]. In the external TCIA dataset, the model had higher reliability than R1 and R2 on full vs reduced dose CT scans [model mean (SD) DSC: 0.96 (0.02), CCC = 0.995 vs R1 DSC: 0.83 (0.07), CCC = 0.89, and R2 DSC:0.87 (0.04), CCC = 0.97]. The DSC and volume concordance correlations for R1 vs R2 (inter-reader reliability) were 0.85 (0.07), CCC = 0.90 at full dose and 0.83 (0.07), CCC = 0.96 at reduced dose datasets. There was good reliability between model and R1 at both full and reduced dose CT [full dose: DSC: 0.81 (0.07), CCC = 0.83 and reduced dose DSC:0.81 (0.08), CCC = 0.87]. Likewise, there was good reliability between model and R2 at both full and reduced dose CT [full dose: DSC: 0.84 (0.05), CCC = 0.89 and reduced dose DSC:0.83(0.06), CCC = 0.89]. There was no difference in model-predicted and GT pancreatic volume in TCIA dataset (mean predicted volume 96 cc (33); GT pancreatic volume 89 cc (30), p = 0.31). Model had mean (SD) DSC: 0.89 (0.04) (minimum-maximum DSC: 0.79 -0.96) on the NIH-PCT dataset. CONCLUSION: A 3D CNN developed on the largest dataset of CTs is accurate for fully automated volumetric pancreas segmentation and is generalizable across a wide range of CT slice thicknesses, radiation dose, and patient gender. This 3D CNN offers a scalable tool to leverage biomarkers from pancreas morphometrics and radiomics for pancreatic diseases including for early pancreatic cancer detection.


Assuntos
Aprendizado Profundo , Feminino , Humanos , Processamento de Imagem Assistida por Computador , Masculino , Pâncreas/diagnóstico por imagem , Doses de Radiação , Reprodutibilidade dos Testes , Tomografia Computadorizada por Raios X
10.
Clin Cancer Res ; 27(9): 2523-2532, 2021 May 01.
Artigo em Inglês | MEDLINE | ID: mdl-33593879

RESUMO

PURPOSE: We have previously identified tissue methylated DNA markers (MDMs) associated with pancreatic ductal adenocarcinoma (PDAC). In this case-control study, we aimed to assess the diagnostic performance of plasma MDMs for PDAC. EXPERIMENTAL DESIGN: Thirteen MDMs (GRIN2D, CD1D, ZNF781, FER1L4, RYR2, CLEC11A, AK055957, LRRC4, GH05J042948, HOXA1, PRKCB, SHISA9, and NTRK3) were identified on the basis of selection criteria applied to results of prior tissue experiments and assays were optimized in plasma. Next, 340 plasma samples (170 PDAC cases and 170 controls) were assayed using target enrichment long-probe quantitative amplified signal method. Initially, 120 advanced-stage PDAC cases and 120 healthy controls were used to train a prediction algorithm at 97.5% specificity using random forest modeling. Subsequently, the locked algorithm derived from the training set was applied to an independent blinded test set of 50 early-stage PDAC cases and 50 controls. Finally, data from all 340 patients were combined, and cross-validated. RESULTS: The cross-validated area under the receiver operating characteristic curve (AUC) for the training set was 0.93 (0.89-0.96) for the MDM panel alone, 0.91 (95% confidence interval, 0.87-0.96) for carbohydrate antigen 19-9 (CA19-9) alone, and 0.99 (0.98-1) for the combined MDM-CA19-9 panel. In the test set of early-stage PDAC, the AUC for MDMs alone was 0.84 (0.76-0.92), CA19-9 alone was 0.87 (0.79-0.94), and combined MDM-CA19-9 panel was 0.90 (0.84-0.97) significantly better compared with either MDMs alone or CA19-9 alone (P = 0.0382 and 0.0490, respectively). At a preset specificity of 97.5%, the sensitivity for the combined panel in the test set was 80% (28%-99%) for stage I disease and 82% (68%-92%) for stage II disease. Using the combined datasets, the cross-validated AUC was 0.9 (0.86-0.94) for the MDM panel alone and 0.89 for CA19-9 alone (0.84-0.93) versus 0.97 (0.94-0.99) for the combined MDM-CA19-9 panel (P ≤ 0.0001). Overall, cross-validated sensitivity of MDM-CA19-9 panel was 92% (83%-98%), with an observed specificity of 92% at the preset specificity of 97.5%. CONCLUSIONS: Plasma MDMs in combination with CA19-9 detect PDAC with significantly higher accuracy compared with either biomarker individually.

11.
Endosc Ultrasound ; 10(1): 39-50, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33473044

RESUMO

Background and Objectives: No single optimal test reliably determines the pancreatic cyst subtype. Following EUS-FNA, the "string sign" test can differentiate mucinous from nonmucinous cysts. However, the interobserver variability of string sign results has not been studied. Methods: An experienced endosonographer performed EUS-FNA of pancreatic cysts on different patients and was recorded on video performing the string sign test for each. The videos were shared internationally with 14 experienced endosonographers, with a survey for each video: "Is the string sign positive?" and "If the string sign is positive, what is the length of the formed string?" Also asked "What is the cutoff length for string sign to be considered positive?" Interobserver variability was assessed using the kappa statistic (κ). Results: A total of 112 observations were collected from 14 endosonographers. Regarding string sign test positivity, κ was 0.6 among 14 observers indicating good interrater agreement (P < 0.001) while κ was 0.38 when observers were compared to the index endosonographer demonstrating marginal agreement (P < 0.001). Among observations of the length of the string in positive samples, 89.8% showed >5 mm of variability (P < 0.001), indicating marked variability. There was poor agreement on the cutoff length for a string to be considered positive. Conclusion: String sign of pancreatic cysts has a good interobserver agreement regarding its positivity that can help in differentiating mucinous from nonmucinous pancreatic cysts. However, the agreement is poor on the measured length of the string and the cutoff length of the formed string to be considered a positive string sign.

12.
Gut ; 70(7): 1335-1344, 2021 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-33028668

RESUMO

OBJECTIVE: The diagnosis of autoimmune pancreatitis (AIP) is challenging. Sonographic and cross-sectional imaging findings of AIP closely mimic pancreatic ductal adenocarcinoma (PDAC) and techniques for tissue sampling of AIP are suboptimal. These limitations often result in delayed or failed diagnosis, which negatively impact patient management and outcomes. This study aimed to create an endoscopic ultrasound (EUS)-based convolutional neural network (CNN) model trained to differentiate AIP from PDAC, chronic pancreatitis (CP) and normal pancreas (NP), with sufficient performance to analyse EUS video in real time. DESIGN: A database of still image and video data obtained from EUS examinations of cases of AIP, PDAC, CP and NP was used to develop a CNN. Occlusion heatmap analysis was used to identify sonographic features the CNN valued when differentiating AIP from PDAC. RESULTS: From 583 patients (146 AIP, 292 PDAC, 72 CP and 73 NP), a total of 1 174 461 unique EUS images were extracted. For video data, the CNN processed 955 EUS frames per second and was: 99% sensitive, 98% specific for distinguishing AIP from NP; 94% sensitive, 71% specific for distinguishing AIP from CP; 90% sensitive, 93% specific for distinguishing AIP from PDAC; and 90% sensitive, 85% specific for distinguishing AIP from all studied conditions (ie, PDAC, CP and NP). CONCLUSION: The developed EUS-CNN model accurately differentiated AIP from PDAC and benign pancreatic conditions, thereby offering the capability of earlier and more accurate diagnosis. Use of this model offers the potential for more timely and appropriate patient care and improved outcome.

13.
Dig Dis Sci ; 66(1): 78-87, 2021 01.
Artigo em Inglês | MEDLINE | ID: mdl-32112260

RESUMO

BACKGROUND: The risk of pancreatic cancer is elevated among people with new-onset diabetes (NOD). Based on Rochester Epidemiology Project Data, the Enriching New-Onset Diabetes for Pancreatic Cancer (END-PAC) model was developed and validated. AIMS: We validated the END-PAC model in a cohort of patients with NOD using retrospectively collected data from a large integrated health maintenance organization. METHODS: A retrospective cohort of patients between 50 and 84 years of age meeting the criteria for NOD in 2010-2014 was identified. Each patient was assigned a risk score (< 1: low risk; 1-2: intermediate risk; ≥ 3: high risk) based on the values of the predictors specified in the END-PAC model. Patients who developed pancreatic ductal adenocarcinoma (PDAC) within 3 years were identified using the Cancer Registry and California State Death files. Area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were estimated. RESULTS: Out of the 13,947 NOD patients who were assigned a risk score, 99 developed PDAC in 3 years (0.7%). Of the 3038 patients who had a high risk, 62 (2.0%) developed PDAC in 3 years. The risk increased to 3.0% in white patients with a high risk. The AUC was 0.75. At the 3+ threshold, the sensitivity, specificity, PPV, and NPV were 62.6%, 78.5%, 2.0%, and 99.7%, respectively. CONCLUSIONS: It is critical that prediction models are validated before they are implemented in various populations and clinical settings. More efforts are needed to develop screening strategies most appropriate for patients with NOD in real-world settings.


Assuntos
Prestação Integrada de Cuidados de Saúde/normas , Diabetes Mellitus/diagnóstico , Diabetes Mellitus/epidemiologia , Neoplasias Pancreáticas/diagnóstico , Neoplasias Pancreáticas/epidemiologia , Idoso , Idoso de 80 Anos ou mais , Estudos de Coortes , Prestação Integrada de Cuidados de Saúde/tendências , Feminino , Seguimentos , Índice Glicêmico/fisiologia , Humanos , Masculino , Pessoa de Meia-Idade , Sistema de Registros/normas , Estudos Retrospectivos , Fatores de Risco
14.
Endoscopy ; 53(6): 603-610, 2021 06.
Artigo em Inglês | MEDLINE | ID: mdl-32629484

RESUMO

BACKGROUND: Endoscopic intervention for pancreatic fluid collections (PFCs) with disconnected pancreatic duct syndrome (DPDS) has been associated with failures and increased need for additional endoscopic and non-endoscopic interventions. The primary aim of this study was to determine the outcomes of endoscopic ultrasound (EUS)-guided transmural drainage of PFCs in patients with DPDS. METHODS: In patients undergoing EUS-guided drainage of PFCs from January 2013 to January 2018, demographic profiles, procedural indications and details, adverse events, outcomes, and subsequent interventions were retrospectively collected. Overall treatment success was determined by PFC resolution on follow-up imaging or stent removal without recurrence. RESULTS: EUS-guided drainage of PFCs was performed in 141 patients. DPDS was present in 57 of them (40 %) and walled-off necrosis was the most frequent type of PFC (55 %). DPDS was not associated with lower clinical success, increased number of repeat interventions, or increased time to PFC resolution. Patients with DPDS were more likely to be treated with permanent transmural plastic double-pigtail stents (odds ratio [OR] 6.4; 95 % confidence interval [CI] 2.5 - 16.5; P < 0.001). However, when stents were removed, DPDS was associated with increased PFC recurrence after stent removal (OR 8.0; 95 %CI 1.2 - 381.8; P = 0.04). CONCLUSIONS: DPDS frequently occurs in patients with PFCs but does not negatively impact successful resolution. DPDS is associated with increased PFC recurrence after stent removal.


Assuntos
Drenagem , Ductos Pancreáticos , Endossonografia , Humanos , Ductos Pancreáticos/diagnóstico por imagem , Ductos Pancreáticos/cirurgia , Estudos Retrospectivos , Stents , Resultado do Tratamento , Ultrassonografia de Intervenção
15.
Pancreatology ; 20(8): 1592-1597, 2020 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-33036921

RESUMO

OBJECTIVES: Despite substantial morbidity and mortality associated with acute pancreatitis (AP), only one small randomized controlled drug trial (RCT) is available in the past few decades from the United States. Hence, we conducted a single-center, double-blind, placebo-controlled RCT of pentoxifylline in AP. METHODS: A total of 9 doses of oral pentoxifylline 400 mg or placebo tablet, three times daily, was administered within 72 h of diagnosis, using randomization blocks by pharmacy. Primary outcome was a composite outcome including any of the following: death, peripancreatic and/or pancreatic necrosis, infected pancreatic necrosis, persistent organ failure, persistent systemic inflammatory response syndrome, hospital stay longer than 4 days, need for intensive care, and need for intervention for necrosis. RESULTS: Between July 7, 2015, and April 4, 2017, we identified 685 patients with AP, 233 met eligibility criteria and 176 were approached for the study. Of these, 91 (51.7%) declined and finally 45 in pentoxifylline group and 38 in placebo group (83 total) were compared. There were no significant differences in primary outcome (27 [60.0%] vs 15 [39.5%]; P = .06). Pentoxifylline group was not associated with any benefit, but withlonger stay (42% vs. 21%; P = .04) and higher readmission rates (16 %vs 3%; P = .047). CONCLUSIONS: We could not demonstrate superiority of pentoxifylline over placebo. Smaller sample size and inclusion of all types of severity might be the reasons for lack of efficacy. The challenges observed in the present study indicate that, in order to conduct a successful drug trial in AP, a multi center collaboration is essential.


Assuntos
Pancreatite , Pentoxifilina , Inibidores de Fosfodiesterase , Administração Oral , Método Duplo-Cego , Humanos , Pancreatite/tratamento farmacológico , Pentoxifilina/administração & dosagem , Inibidores de Fosfodiesterase/uso terapêutico
16.
Pancreatology ; 20(7): 1495-1501, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32950386

RESUMO

BACKGROUND: The frequency, nature and timeline of changes on thin-slice (≤3 mm) multi-detector computerized tomography (CT) scans in the pre-diagnostic phase of pancreatic ductal adenocarcinoma (PDAC) are unknown. It is unclear if identifying imaging changes in this phase will improve PDAC survival beyond lead time. METHODS: From a cohort of 128 subjects (Cohort A) with CT scans done 3-36 months before diagnosis of PDAC we developed a CTgram defining CT Stages (CTS) I through IV in the radiological progression of pre-diagnostic PDAC. We constructed Cohort B of PDAC resected at CTS I and II and compared survival in CTS I and II in Cohort A (n = 22 each; control natural history cohort) vs Cohort B (n = 33 and 72, respectively; early interception cohort). RESULTS: CTs were abnormal in 16% and 85% at 24-36 and 3-6 months respectively, before PDAC diagnosis. The PDAC CTgram stages, findings and median lead times (months) to clinical diagnosis were: CTS I: Abrupt duct cut-off/duct dilatation (-12.8); CTS II: Low density mass confined to pancreas (-9.5), CTS III: Peri-pancreatic infiltration (-5.8), CTS IV: Distant metastases (only at diagnosis). PDAC survival was better in cohort B than in cohort A despite inclusion of lead time in Cohort A: CTS I (36 vs 17.2 months, p = 0.03), CTS II (35.2 vs 15.3 months, p = 0.04). CONCLUSION: Starting 12-18 months before PDAC diagnosis, progressive and increasingly frequent changes occur on CT scans. Resection of PDAC at the time of pre-diagnostic CT changes is likely to provide survival benefit beyond lead time.


Assuntos
Carcinoma Ductal Pancreático/diagnóstico por imagem , Neoplasias Pancreáticas/diagnóstico por imagem , Tomografia Computadorizada por Raios X/métodos , Idoso , Idoso de 80 Anos ou mais , Carcinoma Ductal Pancreático/cirurgia , Estudos de Coortes , Progressão da Doença , Diagnóstico Precoce , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Tomografia Computadorizada Multidetectores , Estadiamento de Neoplasias , Neoplasias Pancreáticas/cirurgia , Valor Preditivo dos Testes , Prognóstico , Estudos Retrospectivos , Análise de Sobrevida
17.
Abdom Radiol (NY) ; 45(12): 4302-4310, 2020 12.
Artigo em Inglês | MEDLINE | ID: mdl-32939632

RESUMO

PURPOSE: To evaluate the performance of trained technologists vis-à-vis radiologists for volumetric pancreas segmentation and to assess the impact of supplementary training on their performance. METHODS: In this IRB-approved study, 22 technologists were trained in pancreas segmentation on portal venous phase CT through radiologist-led interactive videoconferencing sessions based on an image-rich curriculum. Technologists segmented pancreas in 188 CTs using freehand tools on custom image-viewing software. Subsequent supplementary training included multimedia videos focused on common errors, which were followed by second batch of 159 segmentations. Two radiologists reviewed all cases and corrected inaccurate segmentations. Technologists' segmentations were compared against radiologists' segmentations using Dice-Sorenson coefficient (DSC), Jaccard coefficient (JC), and Bland-Altman analysis. RESULTS: Corrections were made in 71 (38%) cases from first batch [26 (37%) oversegmentations and 45 (63%) undersegmentations] and in 77 (48%) cases from second batch [12 (16%) oversegmentations and 65 (84%) undersegmentations]. DSC, JC, false positive (FP), and false negative (FN) [mean (SD)] in first versus second batches were 0.63 (0.15) versus 0.63 (0.16), 0.48 (0.15) versus 0.48 (0.15), 0.29 (0.21) versus 0.21 (0.10), and 0.36 (0.20) versus 0.43 (0.19), respectively. Differences were not significant (p > 0.05). However, range of mean pancreatic volume difference reduced in the second batch [- 2.74 cc (min - 92.96 cc, max 87.47 cc) versus - 23.57 cc (min - 77.32, max 30.19)]. CONCLUSION: Trained technologists could perform volumetric pancreas segmentation with reasonable accuracy despite its complexity. Supplementary training further reduced range of volume difference in segmentations. Investment into training technologists could augment and accelerate development of body imaging datasets for AI applications.


Assuntos
Inteligência Artificial , COVID-19/prevenção & controle , Competência Clínica/estatística & dados numéricos , Processamento de Imagem Assistida por Computador/métodos , Pâncreas/anatomia & histologia , Tomografia Computadorizada por Raios X/métodos , Conjuntos de Dados como Assunto , Humanos , Radiologia/educação , Reprodutibilidade dos Testes , Estudos Retrospectivos
18.
J Gastroenterol ; 55(11): 1087-1097, 2020 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-32770464

RESUMO

BACKGROUND: The long-term outcomes of immunoglobulin G4-related sclerosing cholangitis (IgG4-SC) are not well known. METHODS: The outcomes of patients with IgG4-SC at Mayo Clinic (1999-2018) were compared to an age- and gender-matched (1:1 ratio) group of patients with primary sclerosing cholangitis (PSC). RESULTS: We identified 89 patients with IgG4-SC; median age at diagnosis was 67 years, 81% were males, and the median follow-up was 5.7 years. Seventy-eight patients received prednisone for induction of remission, and 53 received at least one other immunosuppressive agent for maintenance of remission. Of the IgG4-SC group, 10 died (median time from diagnosis until death was 6.5 years): 2 due to cirrhosis, 3 due to cholangiocarcinoma (CCA), and 5 due to non-hepatobiliary causes. Eleven patients in the PSC group underwent liver transplantation, while none did in the IgG4-SC group. The incidence of a hepatobiliary adverse event (cirrhosis or CCA) was 3.4 times greater in the PSC compared to the IgG4-SC group (events per 1000 person-years: 52.6; 95% CI 38-73; vs. 15.6; 95% CI 7-32). The probability of development of a hepatobiliary adverse event within 10 years was 11% in the IgG4-SC compared to 45% in the PSC group (P = 0.0001). The overall survival tended to be higher in the IgG4-SC compared to the PSC group (10-year: 79% vs. 68%, respectively; P = 0.11). CONCLUSIONS: In a cohort of IgG4-SC patients, 88% of whom were treated with immunosuppressive drugs, the risk of cirrhosis and CCA was significantly lower compared to an age- and gender-matched group with PSC.

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